Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Khaled Bouzenad,Messaoud Ramdani

doi:10.3390/a10020049

Abstract

Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.

Highlights

Increasing sensor availability in process monitoring has led to higher demands on the ability to early detect and identify any sensor faults, especially when the monitoring procedure is based on the information obtained from many sensors
Among the popular multivariate statistical process control, the traditional principal component analysis (PCA) and the independent component analysis (ICA) [10,12,13] are the most early and major methods often used in monitoring, which serve as reference of the desired process behaviour and against which new data can be compared (Rosen and Olsson, 2007)
We propose a robust process monitoring strategy based on Gaussian mixture model (GMM) to extract multiple normal operating modes characterized by m

Summary

Introduction

Increasing sensor availability in process monitoring has led to higher demands on the ability to early detect and identify any sensor faults, especially when the monitoring procedure is based on the information obtained from many sensors. Among the popular multivariate statistical process control, the traditional principal component analysis (PCA) and the independent component analysis (ICA) [10,12,13] are the most early and major methods often used in monitoring, which serve as reference of the desired process behaviour and against which new data can be compared (Rosen and Olsson, 2007). In practice, the process variables follow approximately mixture Gaussian distributions (μ j , σj ) due to process nonlinearity, which gives a multimodal behaviour; an adaptive confidence limit (ACL) is expected to improve the process performance. In this context, we propose a robust process monitoring strategy based on Gaussian mixture model (GMM) to extract multiple normal operating modes characterized by m

Methods

Results

Conclusion